kld_c = ctlr.read_data_grads(my_file,
ctl_dict,
masked=False,
undef2nan=True)
kldgr = dict()
sprdgr = dict()
#Compute KLD growth
for my_var in plot_variables:
kldgr[my_var] = np.squeeze(
np.nanmean(np.delete(kld_c[my_var] - kld_p[my_var], 4, 2),
2)) / delta_data.total_seconds()
#Smooth the growing rate.
kldgr[my_var] = csf.gaussian_smooth_2d(kldgr[my_var], 2.0, 5.0)
#Gues spread at time t
my_file = basedir + expname + ptime.strftime(
"%Y%m%d%H%M%S") + '/guesgp/moment0002.grd'
sprd_p = ctlr.read_data_grads(my_file,
ctl_dict,
masked=False,
undef2nan=True)
#Analysis spread at time t
my_file = basedir + expname + ctime.strftime(
"%Y%m%d%H%M%S") + '/guesgp/moment0002.grd'
sprd_c = ctlr.read_data_grads(my_file,
kld_c = ctlr.read_data_grads(my_file, ctl_dict, masked=False)
#Compute remove undef values, compute kld growth
#and smooth the result.
for my_var in kld_c:
if my_var in plot_variables:
kld_c[my_var][kld_c[my_var] == undef] = 0 #np.nan
kld_p[my_var][kld_p[my_var] == undef] = 0 #np.nan
kld_c[my_var][kld_c[my_var] > 100] = 0 #np.nan
kld_p[my_var][kld_p[my_var] > 100] = 0 #np.nan
kldgr[my_var] = (kld_c[my_var] -
kld_p[my_var]) / delta.total_seconds()
#Smooth the growing rate.
kldgr[my_var][:, :, :, 0] = csf.gaussian_smooth_2d(
kldgr[my_var], 2.0, 5.0)
#Read the ensemble mean to get the information from the storm location.
my_file = basedir + expname + ctime.strftime(
"%Y%m%d%H%M%S") + '/' + my_file_type + '/' + '/moment0001.grd'
ens_mean = ctlr.read_data_grads(my_file, ctl_dict, masked=False)
#Compute max_dbz (we will use this to identify areas associated with clouds and convection)
tmp_max_dbz = np.squeeze(np.nanmax(ens_mean['dbz'], 2))
for ilev in range(0, nlev): #Create a fake 3D array for the max_dbz
#This is because the plotting function expects a 3D array as input.
max_dbz[:, :, ilev] = tmp_max_dbz
#=======================================================================================
corrcoef_norain[my_exp] = dict()
for my_var in plot_variables:
corrcoef_rain[my_exp][my_var] = np.zeros(ntimes)
corrcoef_norain[my_exp][my_var] = np.zeros(ntimes)
kldgr = dict()
sprdgr = dict()
#Compute KLD growth
for my_var in plot_variables:
kld_c[my_var][kld_c[my_var] == undef] = np.nan
kld_p[my_var][kld_p[my_var] == undef] = np.nan
kldgr[my_var] = np.squeeze(
np.nanmean(np.delete(kld_c[my_var] - kld_p[my_var], 4, 2),
2)) / delta_data.seconds
kldgr[my_var] = csf.gaussian_smooth_2d(kldgr[my_var], 2.0, 5.0)
my_file = basedir + '/' + my_exp + '/' + ptime.strftime(
"%Y%m%d%H%M%S") + '/analgp/moment0002.grd'
sprd_p = ctlr.read_data_grads(my_file, ctl_dict, masked=False)
my_file = basedir + '/' + my_exp + '/' + ctime.strftime(
"%Y%m%d%H%M%S") + '/guesgp/moment0002.grd'
sprd_c = ctlr.read_data_grads(my_file, ctl_dict, masked=False)
for my_var in plot_variables:
sprd_c[my_var][sprd_c[my_var] == undef] = np.nan
sprd_p[my_var][sprd_p[my_var] == undef] = np.nan
sprdgr[my_var] = np.squeeze(
